
\section{Conclusion}
\label{sec:conclusion}

An observation that is common with virtually all fields of machine learning
is that heterogeneous training data is important for improving the performance
on unseen samples.

Our results show interesting results on individual aspects and techniques;
a more integrated approach to clustering and matching meta-features to clusters
would therefore be an interesting follow-up project. Especially an integration
of meta-feature mapping based on individual sensor models (rather than merged
ones) and the benefits of relational profiles - as indicated in section
\ref{sec:exprelational} - should give better results.

As to the one-to-one heuristic specifically and on meta-feature matching in
general, it would be interesting to look at examples where one meta-feature
space shared by all domains exists. This is not true in the data we worked with.
One could remove sensors from this data and remap meta-features to easily obtain
such a data set.

Finally, transfer performance should be evaluated on the automatically computed
clusters. Although the mapping doesn't score as favourably as with predefined
meta-features, these unsupervised results might offer a better grouping of
sensors. For instance, in one of the houses the toilet door is also the bathroom
door and is active both when the inhabitant takes a shower and when he uses the
toilet. This resulted in certain cases in the meta-feature ``Toilet'' to be
associated with the meta-feature ``BathroomDoor''. This means that one either
needs a way of associating (sets of) sensors with multiple clusters
(meta-features), or one needs a more coherent clustering. The evaluation of
transfer performance in both cases might give more insight into this issue.

As a last remark it might be worth saying that when the domains are relatively
different - as in our case with House C versus the other two - then transfer of
knowlege might not be that useful. It would therefore be useful to explicitly
study the relationship between mapping accuracy and transfer performance.

